Some authors consider fairness for both users and items, often by applying existing user or item criteria simultaneously to both sides, such as [1]}, [2]}, [3]}. [4]}, [5]} instead discuss two-sided fairness with envy-freeness as user-side criterion, while [6]} propose to use generalized cross entropy to measure unfairness among sensitive groups of users and items. [7]} recently considered two-sided fairness in recommendation as a multi-objective problem, where each objective corresponds to a different fairness notion, either for users or items. Similarly, [8]} aggregate multiple recommendation objectives using a GGF, in a contextual bandit setting. In their case, the aggregated objectives represent various metrics (e.g., clicks, dwell time) for various stakeholders. Unlike these two works [7]}, [8]}, in our case the multiple objectives are the individual utilities of each user and item, and our goal is to be fair towards each entity by redistributing utility. To our knowledge, we are the first to use GGFs as welfare functions of users' and items' utilities for two-sided fairness in rankings.
